preti.david at gmail.com
Work Experience
Academic and Business
Almawave
2020 - Current
Machine Learning Research Engineer
Istituto Nazionale di Fisica Nucleare
2017-2019
PostDoctoral Research Fellow
University of Edimburgh - Higgs Centre for Theoretical Physics
2016
Short Term Scientific Visitor
Consejo Superior De Investigation Cientifica
2014-2017
PreDoctoral Research Fellow
Education
Ph.D. Theoretical Physics
Thesis: Determination of fundamental parameters in the hadronic sector of the Standard Model.
2017
Master of Science - Physics
Thesis: Non-perturbative renormalization of DF=2 four-fermion operators.
2013
Bachelor of Science - Physics
Thesis: Bose-Einstein condensation in atomic trapped gases
2010
PostGraduate Education
CINECA
2019
15th Advanced School on Parallel Computing
Introduction to massively parallel architectures (Cavazzoni/Emerson, CINECA)
Software Engineering for HPC (Ciancarini, CINI)
Cineca HPC systems (Marani, CINECA)
Introduction to GPU Programming (Bonfá, CINECA)
Accelerated programming models: CUDA, OpenACC, CUDAFortran (E4/Arm)
ARM architecture and Ecosystem (Arm)
Compilers and Tools + Optimisation and Performance (Arm)
Python for HPC (Spallanzani, CINECA)
Julich Supercomputing Centre
2015
Lattice Practices 2015
Data analysis (C. Hölbling, Wuppertal U.)
Solvers (A. Frommer and K. Kahl, Wuppertal U.)
Performance Optimization (G. Koutsou, Cyprus Institute)
Computer Architectures (D.Pleiter, JSC/Regensburg U.)
Valence techniques (A. Vaquero, INFN)
Finite temperature QCD (K. Szabo, JSC/Wuppertal U.)
Universita' degli Studi di Parma
2014
VII International School of Theoretical Physics
Introduction to lattice field theories (L. Del Debbio, Edinburgh)
Numerical Methods for lattice QCD (S. Schaefer, DESY NIC)
Lattice Heavy Flavor Physics and Standard Model (M. Della Morte, Odense)
QCD thermodynamics (O. Philipsen, Frankfurt)
Graphene, Topological Insulators and Weyl Semi-metals (P. Buividovich, Regensburg)
Galileo Galilei Institute
2016
Lectures on the Theory of Fundamental Interactions
Early Universe (P. Creminelli, ICTP)
QFT beyond perturbation theory (L. Giusti, Milano Bicocca U. and INFN)
Effective Field Theories (D. B. Kaplan, U. of Washington and INT, Seattle)
Flavor Physics (Y. Nir, Weizmann Inst.)
Dark Matter (S. Profumo, UC, Santa Cruz)
QCD and Collider Physics (M. Schwartz, Harvard U.)
E-learning
Deeplearning.ai
2021
Machine Learning Engineering for Production Specialization
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Introduction to Machine Learning in Production
-
Machine Learning Data Lifecycle in Production
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Machine Learning Modeling Pipelines in Production
-
Deploying Machine Learning Models in Production
Deeplearning.ai
2021
TensorFlow: Advanced Techniques Specialization
Custom Models, Layers, and Loss Functions with TensorFlow
Custom and Distributed Training with TensorFlow
Advanced Computer Vision with TensorFlow
Generative Deep Learning with TensorFlow
Deeplearning.ai
2020
TensorFlow Developer Specialization
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Convolutional Neural Networks in TensorFlow
Natural Language Processing in TensorFlow
Sequences, Time Series and Prediction
Deeplearning.ai
2019
Deep Learning Specialization
Neural Networks and Deep Learning
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Structuring Machine Learning Projects
Convolutional Neural Networks
Sequence Models
Stanford University
2021
Game Theory
Copenhagen Business School
2021
Strategic Management and Innovation Specialization
Strategic Management
Strategy Formulation
Strategy Implementation
Capstone Project
University of Alberta
2020
Reinforcement Learning Specialization
Fundamentals of Reinforcement Learning
Sample-based Learning Methods
Prediction and Control with Function Approximation
A Complete Reinforcement Learning System (Capstone Project)
Udemy
2019 - ongoing
Kubernetes Certified Application Developer (CKAD) with Tests
Udemy
2019
PyTorch for Deep Learning with Python Bootcamp
Udemy
2019
Complete Guide to TensorFlow for Deep Learning with Python
Udemy
2019
NLP - Natural Language Processing with Python
Udemy
2019
Python for Data Science and Machine Learning Bootcamp